{ "cells": [ { "cell_type": "markdown", "id": "b412ef40-3d6a-422d-a658-0f7f9474e423", "metadata": {}, "source": [ "# The heart in the Tenakh (BHSA)" ] }, { "cell_type": "markdown", "id": "8da22526-375c-406c-a63c-bc425007d7e3", "metadata": {}, "source": [ "## Table of content (TOC)\n", "\n", "* 1 - Introduction\n", "* 2 - Load Text-Fabric app and data\n", "* 3 - Performing the queries\n", " * 3.1 - Print table with frequency for lev and levev\n", " * 3.2 - Plotting the frequency per book\n", "* 4 - Required libraries\n", "* 5 - Notebook details\n" ] }, { "cell_type": "markdown", "id": "06785b1d-4bde-40bd-813d-9381f48e8aa4", "metadata": {}, "source": [ "# 1 - Introduction \n", "##### [Back to TOC](#TOC)" ] }, { "cell_type": "markdown", "id": "69c871f8-82b9-47b3-b3cd-95c9a3b7ed12", "metadata": {}, "source": [ "In Hebrew, the words for \"heart\" are written as follows:\n", "\n", "- לֵב (\"lev\"): This is the shorter form commonly used to mean \"heart.\"\n", "- לֵבָב (\"levav\"): This is a slightly longer, more poetic or emphatic form of the word, also meaning \"heart.\"\n", "\n", "This Jupyter NoteBook investigates occurances per book." ] }, { "cell_type": "markdown", "id": "525e2cf9-08e0-4d20-a5f1-b7b7a15662fa", "metadata": {}, "source": [ "# 2 - Load Text-Fabric app and data \n", "##### [Back to TOC](#TOC)" ] }, { "cell_type": "code", "execution_count": 1, "id": "ef428d41-2caa-4522-95cf-ef39e4f0e8da", "metadata": { "tags": [] }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "id": "77b1cb10-629c-4653-b4d0-afc1d13e9d7e", "metadata": {}, "outputs": [], "source": [ "# Loading the Text-Fabric code\n", "# Note: it is assumed Text-Fabric is installed in your environment.\n", "from tf.fabric import Fabric\n", "from tf.app import use" ] }, { "cell_type": "code", "execution_count": 3, "id": "e256d50f-a0d1-4c4c-819d-33bab7fb75c7", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/markdown": [ "**Locating corpus resources ...**" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "app: ~/text-fabric-data/github/etcbc/BHSA/app" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "data: ~/text-fabric-data/github/etcbc/BHSA/tf/2021" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "data: ~/text-fabric-data/github/etcbc/phono/tf/2021" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "data: ~/text-fabric-data/github/etcbc/parallels/tf/2021" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " TF: TF API 12.6.1, etcbc/BHSA/app v3, Search Reference
\n", " Data: etcbc - BHSA 2021, Character table, Feature docs
\n", "
Node types\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "
Name# of nodes# slots / node% coverage
book3910938.21100
chapter929459.19100
lex923046.22100
verse2321318.38100
half_verse451799.44100
sentence637176.70100
sentence_atom645146.61100
clause881314.84100
clause_atom907044.70100
phrase2532031.68100
phrase_atom2675321.59100
subphrase1138501.4238
word4265901.00100
\n", " Sets: no custom sets
\n", " Features:
\n", "
Parallel Passages\n", "
\n", "\n", "
\n", "
\n", "crossref\n", "
\n", "
int
\n", "\n", " 🆗 links between similar passages\n", "\n", "
\n", "\n", "
\n", "
\n", "\n", "
BHSA = Biblia Hebraica Stuttgartensia Amstelodamensis\n", "
\n", "\n", "
\n", "
\n", "book\n", "
\n", "
str
\n", "\n", " ✅ book name in Latin (Genesis; Numeri; Reges1; ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "book@ll\n", "
\n", "
str
\n", "\n", " ✅ book name in amharic (ኣማርኛ)\n", "\n", "
\n", "\n", "
\n", "
\n", "chapter\n", "
\n", "
int
\n", "\n", " ✅ chapter number (1; 2; 3; ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "code\n", "
\n", "
int
\n", "\n", " ✅ identifier of a clause atom relationship (0; 74; 367; ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "det\n", "
\n", "
str
\n", "\n", " ✅ determinedness of phrase(atom) (det; und; NA.)\n", "\n", "
\n", "\n", "
\n", "
\n", "domain\n", "
\n", "
str
\n", "\n", " ✅ text type of clause (? (Unknown); N (narrative); D (discursive); Q (Quotation).)\n", "\n", "
\n", "\n", "
\n", "
\n", "freq_lex\n", "
\n", "
int
\n", "\n", " ✅ frequency of lexemes\n", "\n", "
\n", "\n", "
\n", "
\n", "function\n", "
\n", "
str
\n", "\n", " ✅ syntactic function of phrase (Cmpl; Objc; Pred; ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "g_cons\n", "
\n", "
str
\n", "\n", " ✅ word consonantal-transliterated (B R>CJT BR> >LHJM ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "g_cons_utf8\n", "
\n", "
str
\n", "\n", " ✅ word consonantal-Hebrew (ב ראשׁית ברא אלהים)\n", "\n", "
\n", "\n", "
\n", "
\n", "g_lex\n", "
\n", "
str
\n", "\n", " ✅ lexeme pointed-transliterated (B.:- R;>CIJT B.@R@> >:ELOH ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "g_lex_utf8\n", "
\n", "
str
\n", "\n", " ✅ lexeme pointed-Hebrew (בְּ רֵאשִׁית בָּרָא אֱלֹה)\n", "\n", "
\n", "\n", "
\n", "
\n", "g_word\n", "
\n", "
str
\n", "\n", " ✅ word pointed-transliterated (B.:- R;>CI73JT B.@R@74> >:ELOHI92JM)\n", "\n", "
\n", "\n", "
\n", "
\n", "g_word_utf8\n", "
\n", "
str
\n", "\n", " ✅ word pointed-Hebrew (בְּ רֵאשִׁ֖ית בָּרָ֣א אֱלֹהִ֑ים)\n", "\n", "
\n", "\n", "
\n", "
\n", "gloss\n", "
\n", "
str
\n", "\n", " 🆗 english translation of lexeme (beginning create god(s))\n", "\n", "
\n", "\n", "
\n", "
\n", "gn\n", "
\n", "
str
\n", "\n", " ✅ grammatical gender (m; f; NA; unknown.)\n", "\n", "
\n", "\n", "
\n", "
\n", "label\n", "
\n", "
str
\n", "\n", " ✅ (half-)verse label (half verses: A; B; C; verses: GEN 01,02)\n", "\n", "
\n", "\n", "
\n", "
\n", "language\n", "
\n", "
str
\n", "\n", " ✅ of word or lexeme (Hebrew; Aramaic.)\n", "\n", "
\n", "\n", "
\n", "
\n", "lex\n", "
\n", "
str
\n", "\n", " ✅ lexeme consonantal-transliterated (B R>CJT/ BR>[ >LHJM/)\n", "\n", "
\n", "\n", "
\n", "
\n", "lex_utf8\n", "
\n", "
str
\n", "\n", " ✅ lexeme consonantal-Hebrew (ב ראשׁית֜ ברא אלהים֜)\n", "\n", "
\n", "\n", "
\n", "
\n", "ls\n", "
\n", "
str
\n", "\n", " ✅ lexical set, subclassification of part-of-speech (card; ques; mult)\n", "\n", "
\n", "\n", "
\n", "
\n", "nametype\n", "
\n", "
str
\n", "\n", " ⚠️ named entity type (pers; mens; gens; topo; ppde.)\n", "\n", "
\n", "\n", "
\n", "
\n", "nme\n", "
\n", "
str
\n", "\n", " ✅ nominal ending consonantal-transliterated (absent; n/a; JM, ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "nu\n", "
\n", "
str
\n", "\n", " ✅ grammatical number (sg; du; pl; NA; unknown.)\n", "\n", "
\n", "\n", "
\n", "
\n", "number\n", "
\n", "
int
\n", "\n", " ✅ sequence number of an object within its context\n", "\n", "
\n", "\n", "
\n", "
\n", "otype\n", "
\n", "
str
\n", "\n", " \n", "\n", "
\n", "\n", "
\n", "
\n", "pargr\n", "
\n", "
str
\n", "\n", " 🆗 hierarchical paragraph number (1; 1.2; 1.2.3.4; ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "pdp\n", "
\n", "
str
\n", "\n", " ✅ phrase dependent part-of-speech (art; verb; subs; nmpr, ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "pfm\n", "
\n", "
str
\n", "\n", " ✅ preformative consonantal-transliterated (absent; n/a; J, ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "prs\n", "
\n", "
str
\n", "\n", " ✅ pronominal suffix consonantal-transliterated (absent; n/a; W; ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "prs_gn\n", "
\n", "
str
\n", "\n", " ✅ pronominal suffix gender (m; f; NA; unknown.)\n", "\n", "
\n", "\n", "
\n", "
\n", "prs_nu\n", "
\n", "
str
\n", "\n", " ✅ pronominal suffix number (sg; du; pl; NA; unknown.)\n", "\n", "
\n", "\n", "
\n", "
\n", "prs_ps\n", "
\n", "
str
\n", "\n", " ✅ pronominal suffix person (p1; p2; p3; NA; unknown.)\n", "\n", "
\n", "\n", "
\n", "
\n", "ps\n", "
\n", "
str
\n", "\n", " ✅ grammatical person (p1; p2; p3; NA; unknown.)\n", "\n", "
\n", "\n", "
\n", "
\n", "qere\n", "
\n", "
str
\n", "\n", " ✅ word pointed-transliterated masoretic reading correction\n", "\n", "
\n", "\n", "
\n", "
\n", "qere_trailer\n", "
\n", "
str
\n", "\n", " ✅ interword material -pointed-transliterated (Masoretic correction)\n", "\n", "
\n", "\n", "
\n", "
\n", "qere_trailer_utf8\n", "
\n", "
str
\n", "\n", " ✅ interword material -pointed-transliterated (Masoretic correction)\n", "\n", "
\n", "\n", "
\n", "
\n", "qere_utf8\n", "
\n", "
str
\n", "\n", " ✅ word pointed-Hebrew masoretic reading correction\n", "\n", "
\n", "\n", "
\n", "
\n", "rank_lex\n", "
\n", "
int
\n", "\n", " ✅ ranking of lexemes based on freqnuecy\n", "\n", "
\n", "\n", "
\n", "
\n", "rela\n", "
\n", "
str
\n", "\n", " ✅ linguistic relation between clause/(sub)phrase(atom) (ADJ; MOD; ATR; ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "sp\n", "
\n", "
str
\n", "\n", " ✅ part-of-speech (art; verb; subs; nmpr, ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "st\n", "
\n", "
str
\n", "\n", " ✅ state of a noun (a (absolute); c (construct); e (emphatic).)\n", "\n", "
\n", "\n", "
\n", "
\n", "tab\n", "
\n", "
int
\n", "\n", " ✅ clause atom: its level in the linguistic embedding\n", "\n", "
\n", "\n", "
\n", "
\n", "trailer\n", "
\n", "
str
\n", "\n", " ✅ interword material pointed-transliterated (& 00 05 00_P ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "trailer_utf8\n", "
\n", "
str
\n", "\n", " ✅ interword material pointed-Hebrew (־ ׃)\n", "\n", "
\n", "\n", "
\n", "
\n", "txt\n", "
\n", "
str
\n", "\n", " ✅ text type of clause and surrounding (repetion of ? N D Q as in feature domain)\n", "\n", "
\n", "\n", "
\n", "
\n", "typ\n", "
\n", "
str
\n", "\n", " ✅ clause/phrase(atom) type (VP; NP; Ellp; Ptcp; WayX)\n", "\n", "
\n", "\n", "
\n", "
\n", "uvf\n", "
\n", "
str
\n", "\n", " ✅ univalent final consonant consonantal-transliterated (absent; N; J; ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "vbe\n", "
\n", "
str
\n", "\n", " ✅ verbal ending consonantal-transliterated (n/a; W; ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "vbs\n", "
\n", "
str
\n", "\n", " ✅ root formation consonantal-transliterated (absent; n/a; H; ...)\n", "\n", "
\n", "\n", "
\n", "
\n", "verse\n", "
\n", "
int
\n", "\n", " ✅ verse number\n", "\n", "
\n", "\n", "
\n", "
\n", "voc_lex\n", "
\n", "
str
\n", "\n", " ✅ vocalized lexeme pointed-transliterated (B.: R;>CIJT BR> >:ELOHIJM)\n", "\n", "
\n", "\n", "
\n", "
\n", "voc_lex_utf8\n", "
\n", "
str
\n", "\n", " ✅ vocalized lexeme pointed-Hebrew (בְּ רֵאשִׁית ברא אֱלֹהִים)\n", "\n", "
\n", "\n", "
\n", "
\n", "vs\n", "
\n", "
str
\n", "\n", " ✅ verbal stem (qal; piel; hif; apel; pael)\n", "\n", "
\n", "\n", "
\n", "
\n", "vt\n", "
\n", "
str
\n", "\n", " ✅ verbal tense (perf; impv; wayq; infc)\n", "\n", "
\n", "\n", "
\n", "
\n", "mother\n", "
\n", "
none
\n", "\n", " ✅ linguistic dependency between textual objects\n", "\n", "
\n", "\n", "
\n", "
\n", "oslots\n", "
\n", "
none
\n", "\n", " \n", "\n", "
\n", "\n", "
\n", "
\n", "\n", "
Phonetic Transcriptions\n", "
\n", "\n", "
\n", "
\n", "phono\n", "
\n", "
str
\n", "\n", " 🆗 phonological transcription (bᵊ rēšˌîṯ bārˈā ʔᵉlōhˈîm)\n", "\n", "
\n", "\n", "
\n", "
\n", "phono_trailer\n", "
\n", "
str
\n", "\n", " 🆗 interword material in phonological transcription\n", "\n", "
\n", "\n", "
\n", "
\n", "\n", " Settings:
specified
  1. apiVersion: 3
  2. appName: etcbc/BHSA
  3. appPath: C:/Users/tonyj/text-fabric-data/github/etcbc/BHSA/app
  4. commit: gd905e3fb6e80d0fa537600337614adc2af157309
  5. css: ''
  6. dataDisplay:
    • exampleSectionHtml:<code>Genesis 1:1</code> (use <a href=\"https://github.com/{org}/{repo}/blob/master/tf/{version}/book%40en.tf\" target=\"_blank\">English book names</a>)
    • excludedFeatures:
      • g_uvf_utf8
      • g_vbs
      • kq_hybrid
      • languageISO
      • g_nme
      • lex0
      • is_root
      • g_vbs_utf8
      • g_uvf
      • dist
      • root
      • suffix_person
      • g_vbe
      • dist_unit
      • suffix_number
      • distributional_parent
      • kq_hybrid_utf8
      • crossrefSET
      • instruction
      • g_prs
      • lexeme_count
      • rank_occ
      • g_pfm_utf8
      • freq_occ
      • crossrefLCS
      • functional_parent
      • g_pfm
      • g_nme_utf8
      • g_vbe_utf8
      • kind
      • g_prs_utf8
      • suffix_gender
      • mother_object_type
    • noneValues:
      • none
      • unknown
      • no value
      • NA
  7. docs:
    • docBase: {docRoot}/{repo}
    • docExt: ''
    • docPage: ''
    • docRoot: https://{org}.github.io
    • featurePage: 0_home
  8. interfaceDefaults: {}
  9. isCompatible: True
  10. local: local
  11. localDir: C:/Users/tonyj/text-fabric-data/github/etcbc/BHSA/_temp
  12. provenanceSpec:
    • corpus: BHSA = Biblia Hebraica Stuttgartensia Amstelodamensis
    • doi: 10.5281/zenodo.1007624
    • moduleSpecs:
      • :
        • backend: no value
        • corpus: Phonetic Transcriptions
        • docUrl:https://nbviewer.jupyter.org/github/etcbc/phono/blob/master/programs/phono.ipynb
        • doi: 10.5281/zenodo.1007636
        • org: etcbc
        • relative: /tf
        • repo: phono
      • :
        • backend: no value
        • corpus: Parallel Passages
        • docUrl:https://nbviewer.jupyter.org/github/etcbc/parallels/blob/master/programs/parallels.ipynb
        • doi: 10.5281/zenodo.1007642
        • org: etcbc
        • relative: /tf
        • repo: parallels
    • org: etcbc
    • relative: /tf
    • repo: BHSA
    • version: 2021
    • webBase: https://shebanq.ancient-data.org/hebrew
    • webHint: Show this on SHEBANQ
    • webLang: la
    • webLexId: True
    • webUrl:{webBase}/text?book=<1>&chapter=<2>&verse=<3>&version={version}&mr=m&qw=q&tp=txt_p&tr=hb&wget=v&qget=v&nget=vt
    • webUrlLex: {webBase}/word?version={version}&id=<lid>
  13. release: v1.8
  14. typeDisplay:
    • clause:
      • label: {typ} {rela}
      • style: ''
    • clause_atom:
      • hidden: True
      • label: {code}
      • level: 1
      • style: ''
    • half_verse:
      • hidden: True
      • label: {label}
      • style: ''
      • verselike: True
    • lex:
      • featuresBare: gloss
      • label: {voc_lex_utf8}
      • lexOcc: word
      • style: orig
      • template: {voc_lex_utf8}
    • phrase:
      • label: {typ} {function}
      • style: ''
    • phrase_atom:
      • hidden: True
      • label: {typ} {rela}
      • level: 1
      • style: ''
    • sentence:
      • label: {number}
      • style: ''
    • sentence_atom:
      • hidden: True
      • label: {number}
      • level: 1
      • style: ''
    • subphrase:
      • hidden: True
      • label: {number}
      • style: ''
    • word:
      • features: pdp vs vt
      • featuresBare: lex:gloss
  15. writing: hbo
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TF API: names N F E L T S C TF Fs Fall Es Eall Cs Call directly usable
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# load the BHSL app and data\n", "BHS = use (\"etcbc/BHSA\",hoist=globals())" ] }, { "cell_type": "markdown", "id": "d32502e9-c6ae-45d6-ac6f-e298b4315bde", "metadata": {}, "source": [ "Note: Thefeature documentation can be found at [ETCBC GitHub](https://github.com/ETCBC/bhsa/blob/master/docs/features/0_home.md) " ] }, { "cell_type": "code", "execution_count": 4, "id": "20826b6e-5511-448d-a0da-8abdbd67eb77", "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# The following will push the Text-Fabric stylesheet to this notebook (to facilitate proper display with notebook viewer)\n", "BHS.dh(BHS.getCss())" ] }, { "cell_type": "markdown", "id": "947d7fd8-20c9-4b82-8b37-45ca74d18ef4", "metadata": {}, "source": [ "# 3 - Performing the queries \n", "##### [Back to TOC](#TOC)" ] }, { "cell_type": "markdown", "id": "c050c0d9-0e4d-4341-afb1-8c8aeaa5b52b", "metadata": {}, "source": [ "## 3.1 - Print table with frequency for lev and levev " ] }, { "cell_type": "code", "execution_count": 5, "id": "45f0cfbf-86e9-47c4-89be-3b31a1b6b85f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.57s 861 results\n" ] } ], "source": [ "levQuery = '''\n", "book\n", " chapter\n", " verse\n", " word lex=LBB/|LB/ \n", "'''\n", "\n", "levResults = BHS.search(levQuery)" ] }, { "cell_type": "code", "execution_count": 6, "id": "db9f9a08-7e9e-4fea-92f8-bc9baf2220d4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BookTotalLevLevav
24Psalmi13710235
26Proverbia99972
12Jeremia66588
4Deuteronomium51447
11Jesaia493118
13Ezechiel47416
1Exodus47461
36Chronica_II441628
29Ecclesiastes42411
9Reges_I371423
7Samuel_I301614
25Iob29209
35Chronica_I20812
8Samuel_II20182
6Judices16142
0Genesis16133
32Daniel15312
10Reges_II1468
14Hosea1091
30Threni1091
5Josua927
34Nehemia761
22Sacharia642
21Haggai505
3Numeri541
31Esther440
23Maleachi440
2Leviticus303
20Zephania312
33Esra321
28Canticum330
15Joel202
19Nahum211
17Obadia220
27Ruth220
18Jona101
16Amos110
\n", "
" ], "text/plain": [ " Book Total Lev Levav\n", "24 Psalmi 137 102 35\n", "26 Proverbia 99 97 2\n", "12 Jeremia 66 58 8\n", "4 Deuteronomium 51 4 47\n", "11 Jesaia 49 31 18\n", "13 Ezechiel 47 41 6\n", "1 Exodus 47 46 1\n", "36 Chronica_II 44 16 28\n", "29 Ecclesiastes 42 41 1\n", "9 Reges_I 37 14 23\n", "7 Samuel_I 30 16 14\n", "25 Iob 29 20 9\n", "35 Chronica_I 20 8 12\n", "8 Samuel_II 20 18 2\n", "6 Judices 16 14 2\n", "0 Genesis 16 13 3\n", "32 Daniel 15 3 12\n", "10 Reges_II 14 6 8\n", "14 Hosea 10 9 1\n", "30 Threni 10 9 1\n", "5 Josua 9 2 7\n", "34 Nehemia 7 6 1\n", "22 Sacharia 6 4 2\n", "21 Haggai 5 0 5\n", "3 Numeri 5 4 1\n", "31 Esther 4 4 0\n", "23 Maleachi 4 4 0\n", "2 Leviticus 3 0 3\n", "20 Zephania 3 1 2\n", "33 Esra 3 2 1\n", "28 Canticum 3 3 0\n", "15 Joel 2 0 2\n", "19 Nahum 2 1 1\n", "17 Obadia 2 2 0\n", "27 Ruth 2 2 0\n", "18 Jona 1 0 1\n", "16 Amos 1 1 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Libraries for table formatting and data display\n", "import pandas as pd\n", "from IPython.display import display\n", "\n", "# Initialize dictionary for storing results\n", "resultDict = {}\n", "\n", "# Process each item in the levResults\n", "for item in levResults:\n", " book = F.book.v(item[0])\n", " lex = F.lex.v(item[3])\n", " \n", " if book in resultDict:\n", " # If it exists, add the count to the existing value\n", " resultDict[book][0] += 1\n", " if lex == 'LB/':\n", " resultDict[book][1] += 1\n", " else:\n", " resultDict[book][2] += 1\n", " else:\n", " # If it doesn't exist, initialize the count as the value\n", " if lex == 'LB/':\n", " resultDict[book] = [1, 1, 0]\n", " else:\n", " resultDict[book] = [1, 0, 1]\n", "\n", "# Convert the dictionary into a DataFrame and sort by total frequency\n", "tableData = pd.DataFrame(\n", " [[key, value[0], value[1], value[2]] for key, value in resultDict.items()],\n", " columns=[\"Book\", \"Total\", \"Lev\", \"Levav\"]\n", ")\n", "tableData = tableData.sort_values(by=\"Total\", ascending=False)\n", "\n", "# Display the table\n", "display(tableData)" ] }, { "cell_type": "markdown", "id": "8c99aab2-ed14-4665-b208-ceaeb8a4cb30", "metadata": {}, "source": [ "## 3.2 - Plotting the frequency per book " ] }, { "cell_type": "code", "execution_count": 7, "id": "ba4e773f-1d2a-430f-82ca-231bcc38bd76", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "\n", "# Sample data setup (replace with your actual data)\n", "# Initialize dictionary for storing results\n", "resultDict = {}\n", "\n", "# Process each item in the levResults\n", "for item in levResults:\n", " book = F.book.v(item[0])\n", " lex = F.lex.v(item[3])\n", " \n", " if book in resultDict:\n", " # If it exists, add the count to the existing value\n", " resultDict[book][0] += 1\n", " if lex == 'LB/':\n", " resultDict[book][1] += 1\n", " else:\n", " resultDict[book][2] += 1\n", " else:\n", " # If it doesn't exist, initialize the count as the value\n", " if lex == 'LB/':\n", " resultDict[book] = [1, 1, 0]\n", " else:\n", " resultDict[book] = [1, 0, 1]\n", "\n", "# Convert the dictionary into a DataFrame and sort by total frequency\n", "tableData = pd.DataFrame(\n", " [[key, value[0], value[1], value[2]] for key, value in resultDict.items()],\n", " columns=[\"Book\", \"Total\", \"LB\", \"LBB\"]\n", ")\n", "\n", "# Set up the data for plotting with LB as positive and LBB as negative values\n", "tableData['LB_Positive'] = tableData['LB'] # LB counts as positive values\n", "tableData['LBB_Negative'] = -tableData['LBB'] # LBB counts as negative values\n", "\n", "# Set up the plot\n", "plt.figure(figsize=(14, 10))\n", "\n", "# Plot LB (positive values)\n", "lb_bars = plt.barh(tableData[\"Book\"], tableData[\"LB_Positive\"], color='blue', label='לֵב')\n", "\n", "# Plot LBB (negative values)\n", "lbb_bars = plt.barh(tableData[\"Book\"], tableData[\"LBB_Negative\"], color='orange', label='לֵבָב')\n", "\n", "# Add the counts on each bar\n", "for bar, count in zip(lb_bars, tableData['LB']):\n", " plt.text(bar.get_width() + 1, bar.get_y() + bar.get_height()/2, str(count), va='center', color='blue')\n", "\n", "for bar, count in zip(lbb_bars, tableData['LBB']):\n", " plt.text(bar.get_width() - 1, bar.get_y() + bar.get_height()/2, str(count), va='center', ha='right', color='orange')\n", "\n", "# Customize x-axis to show absolute values\n", "x_ticks = plt.xticks()[0] # Get current tick positions\n", "plt.xticks(x_ticks, [str(int(abs(x))) for x in x_ticks]) # Set absolute values for labels\n", "\n", "# Add labels, title, and legend\n", "plt.xlabel(\"Count\")\n", "plt.ylabel(\"Book\")\n", "plt.title('Distribution of לֵב and לֵבָב Counts per Book in the Tenach')\n", "plt.axvline(0, color=\"black\", linewidth=0.5) # Center line at x=0\n", "plt.legend(title=\"Category\")\n", "\n", "# Show the plot\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "5d859352-6a31-439f-b20e-92edab9f59fe", "metadata": { "tags": [] }, "source": [ "# 4 - Required libraries\n", "##### [Back to TOC](#TOC)\n", "\n", "The scripts in this notebook require (beside `text-fabric`) the following Python libraries to be installed in the environment:\n", "\n", " IPython\n", " pandas\n", " matplotlib\n", " seaborn\n", "\n", "You can install any missing library from within Jupyter Notebook using either`pip` or `pip3`." ] }, { "cell_type": "markdown", "id": "ca0de1ed-fb4c-484a-bbbd-3322d321f40e", "metadata": {}, "source": [ "# 5 - Notebook details\n", "##### [Back to TOC](#TOC)\n", "\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AuthorTony Jurg
Version1.0
Date4 Novermber 2024
\n", "
" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }